Sepsis, a critical condition that arises from organ dysfunction due to severe infection, can progress to severe sepsis and septic shock, leading to multi-organ failure and increased mortality rates. The complexity of diagnosing sepsis stems from the absence of a definitive test, with current detection methods depending on broad-ranged biomarkers such as CRP, PCT, and lactate levels. The disease's variability and the general approach of administering broad-spectrum antibiotics, antivirals, and antifungals underscore the urgency for more specific diagnostic and treatment strategies. New research to be presented at ECCMID 2024 highlights the success achieved by researchers in identifying distinct molecular signatures associated with the clinical signs of sepsis that could enable more accurate diagnosis and prognosis of the condition, as well as help design targeted therapies for patients who stand to benefit the most.

In this study, researchers from Lund University (Lund, Sweden) analyzed plasma samples collected over a period of five years from 1,364 adults suspected of sepsis upon their arrival at the emergency department. Of these, 913 were diagnosed with sepsis out of 1,073 who had infections. Through mass spectrometry, the researchers developed detailed molecular profiles, enabling them to identify protein patterns that accurately predict septic shock. This information was used to create a machine-learning model, categorizing patients into risk groups for developing septic shock, thus demonstrating the model's potential to predict sepsis severity and associated mortality risks accurately.

Furthermore, the researchers identified protein panels indicative of six organ dysfunctions (cardiac, CNS, coagulation, liver, kidney, respiratory) and various infections, influencing the distinct proteomic pathways influencing sepsis. Risk classifications based on organ dysfunction and infection probabilities offered insights into the mortality risks, paving the way for targeted therapeutic interventions. However, the researchers acknowledged the study's limitations, such as the need for validation across diverse cohorts and the dynamic nature of sepsis requiring continuous monitoring. This research marks a significant step toward advancing the understanding and management of sepsis, emphasizing the need for further studies to explore the progression of proteomic changes in sepsis over time.

“A fast test that provides more accurate sepsis diagnosis and could also predict who is at greater risk of poorer outcomes now seems a genuine possibility”, said co-lead author Dr. Lisa Mellhammar from Lund University. “Any research like this needs clinical validation and many hurdles must be cleared before these biomarkers are used in the clinic. But we envision this as a tool that could be deployed worldwide, as the future of early detection of sepsis.”

Source:
https://www.labmedica.com/microbiology/articles/294800748/simple-blood-test-combined-with-personalized-risk-model-improves-sepsis-diagnosis.html